In [1]}, a 3D fully convolutional neural network is proposed to extract the feature of hyperspectral images. In [2]}, Li et al. proposed a grouped deep recursive residual network by designing a group recursive module and embedding it into a global residual structure. In [3]}, an unsupervised CNN-based method is proposed to effectively exploit the underlying characteristics of the hyperspectral images. In [4]}, Jiang et al. proposed a group convolution and progressive upsampling framework to reduce the size of the model and made it feasible to obtain stable training results under small data conditions. In [5]}, a Spectral Grouping and Attention-Driven Residual Dense Network is proposed to facilitate the modeling of all spectral bands and focus on the exploration of spatial-spectral features.
